This is the code for lane detection based on the bsnake paper by Wang et al.
- It described the perspective effect of parallel lines is constructed with dual external forces for generic lane boundary or marking
- It is able to describe a wider range of lane structures than other lane models such as straight and parabolic models.
- It is robust against shadows, noises and more due to the use of parallel knowledge of roads on the ground plane.
- The lane detection problem is formulated by determining the set of lane model control points.
- It is a robust algorithm presented for providing a good initial position for B-Snake lane Model which robust to noises, shadows, and illumination variations in the captured road images.
- It is also applicable to both marked and unmarked, dash paint line and solid paint line roads.
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The road is assumed to have two parallel boundaries on the ground, and in the short horizontal band of image, the road is approximately straight. As a result of the perspective projection, the road boundaries in the image plane should intersect at a shared vanishing point on the horizon.
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There are five processing stages in CHEVP algorithms:
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Edge pixel extraction by Canny edge detection. Canny edge detection is employed to obtain edge map.
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Straight lines detection by hough transform.
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Horizon and vanishing detection.
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Estimation the mid-line of road and the parameter k by the detected road lines.
$$k=c_right-c_left/r_mid-hz$$
hz = vertical coordinate of the vanishing line
- Initial the control points of the lane model to approach the mid-line detected by last step.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.6644&rep=rep1&type=pdf